Evaluation of our
Decision Support System
Table of Contents
1-Evaluation of the theory and practice
1.3-History
Decision support systems (DSS’s) have seen many changes. It was not until the 1960’s when the first model oriented DSS were built (Power 2003). As technology improved Gorry and Scott (1971) cited in (Olson & Courtney, 1992) noted that DSS’s have been able to cope with more un-structured problems. The 1990’s saw the introduction of desktop online application processor’s (DOLAP), Database Management Systems (DBMS), data warehousing and the Internet, which were all incorporated into many DSSs.
1.1-Introduction
There are many decisions to consider for a company such as Nightjar entering into the small package delivery sector. For example where to place its first nodes? What to offer? All these questions need answering and to aid decision making DSS’s can be used (Turban & Aranson 1998).
1.2-Definition
Throughout the years many theorists have defined DSS’s. Watson et al. (1997, p263) defines DSS’s as an “interactive system that provides the user with easy access to decision models and data in order to support semi structured and unstructured decision-making tasks”.
1.5-Elements
There are four elements of a DSS (Appendix 1).
1.5.1-Data Base Model
The database Model needs to be provided with up to date information in order to perform correctly. A database model consists of four elements, (appendix 2) and its main purpose is to organise related data enabling users to define, create and maintain databases. The data directory provides data definitions, while the query facility allows users to search for specific areas. An example is MSAccess.
Normalisation is a modelling process among relational databases where the relations or tables are decomposed into smaller relations to a point where all attributes in a relation are coupled with the primary key of the relation (Bostrup 2002). This is beneficial as it breaks down data into smaller parts but still keeps them related.
1.5.2-Model Base
A model base consists of many different components (appendix 3) and provides a DSS with the analysis capabilities to provide a true reflection of actual situations by providing routine and special statistical, financial, forecasting, and management science models (Turban 1995). An example is solver within Excel.
1.5.3-Knowledge Base
All DSS’s need a knowledge base in order for the other bases to run accurately. This can be provided either by human interface or a computer system such as an expert system or artificial intelligence. The knowledge base subsystem also needs software in order for it to integrate with other components (Turban 1995). For example Lotus notes
1.5.4-User Interface Base
This base communicates between the DSS and the user. According to Whitten & Bentley (1997) cited in Turban E & Aranson J (1998) this is the most important part of any DSS as it is the only part that the user sees. The system communicates with the user through a user interface management system (UIMS); which uses standard objects (pull down menus) through a graphical user interface (GUI) (Appendix 4).
1.6-Types
There are two types in which all others DSS’s fall into (Power 1997).
- Enterprise-wide DSS’s are incorporated to serve the whole organisation. Example ...
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1.5.4-User Interface Base
This base communicates between the DSS and the user. According to Whitten & Bentley (1997) cited in Turban E & Aranson J (1998) this is the most important part of any DSS as it is the only part that the user sees. The system communicates with the user through a user interface management system (UIMS); which uses standard objects (pull down menus) through a graphical user interface (GUI) (Appendix 4).
1.6-Types
There are two types in which all others DSS’s fall into (Power 1997).
- Enterprise-wide DSS’s are incorporated to serve the whole organisation. Example being the decision Makers workbench (DMW) (Power 1997).
- Desktop DSS’s are for individual users and are more specialised. Example being expert choice (www.expertchoice.com 2003).
The type of DSS you require will depend on what decisions you need answering. DSS’s fall into five categories (Power 1997).
- Communication driven – These types of DSS’s allow multiply users to communicate and share information.
- Data driven – Usually consists of a data warehouse and OLAP software to allow managers access and manipulate data.
- Document driven – Similar to Data driven but focuses on allowing access to and management of unstructured documents.
- Knowledge driven – These types are normally incorporated into an expert system to give managers detailed advise on a specialised area.
- Model driven – Use statistical, financial, optimization and/or simulation models to aid decision making for managers.
1.8-Group DSS’s
GDSS’s allows multiply users to analyse a given problem simultaneously by assisting in the decision making process (Appendix 5). An example is video conferencing software, which is useful within meetings and allows interaction.
1.9-Data Warehousing
A data warehouse is a large multidimensional database that contains company data, which can be stored and shared among an organisation. There are many techniques that can be used to aid decision-making. For ‘what’ questions a query of the data would be enough, however for ‘why’ and ‘how’ questions data mining would be more appropriate. This technique is a process of looking for specific information to predict or identify trends. Also Online analytical processing (OLAP) tools such as ‘drill down’ and ‘slicing and dicing’ are being used to aid decision-making.
1.10-Associative databases
For an organisation that can’t afford large data warehouses a cheaper alternative is a customised database such as lazysoft’s associative model of data. This model uses a single generic structure to contain all types of data. Lazysoft’s sentences product is a multi-user; web enabled transactional database management system (www.lazysoft.com 2003).
2-Evaluation of my system
2.1-In light of the theory.
Our DSS is a desktop DSS and classified as a hybrid type because it is both data and model driven. We gathered vast amounts of data to enable us to build a database within MSAccess. This data modelled by functions such as Solver within MSExcel aloud our DSS to provide our users with support when making decisions.
2.1.1-Elements
2.1.1.1 Data base - Due to the small scale of our DSS a data warehouse would have been expensive. Our Database was created using MSAccess which is a relational database package that allows us to normalise databases together through key fields fig, 1 shows our database relationships.
Fig, 1.
2.1.1.2-Model base
Our model base was based on MSExcel functions to forecast results that would help users make their decisions. The functions that we used include Solver to calculate the end cost of each route, SUMIF which aloud us to add cells that we wanted to specify by a criteria and VLOOKUP which aloud us to look up different values and use them within a formula. There were limitations to our model base, the solver function only allowed a maximum of 200 arcs between towns. This decreased the professionalism of our system, as routes were not as accurate as we would have liked.
2.1.1.3-Knowledge base
Our knowledge base was provided by human interface and supported by Lotus notes. Between ourselves we provided our own knowledge base. It would have been too expensive to incorporate a computer based knowledge system. However if our DSS was on a larger scale or was incorporated into other systems then it might be beneficial to include computerised support.
2.1.1.4-User interface Base
Our graphical user interface (GUI) was important and we tried to make it as user friendly as possible. It consists of a JIP map of the world with a hyperlink over each city. This allows the user to visually see where the two cities are on a map. If the user were to double click on a city using the mouse, it will show all the data in a graphical table (object) for the user to analyse. This allows them to gather information before determining a route. Our system also had many standard objects such as pull down menus and buttons to help guide the user through the process. Our GUI is based within MSAccess. A constraint our User interface had was that to calculate the route the user had to manually go into excel to click on solver.
2.2-Compared to other DSS’s on the market.
Other systems on the market such as FedEx have more money and expertise spend on them; also we had a time constraint of 8 weeks. However if our system went on the market I feel it would be popular among customer because of its friendly user-face. As for example FedEx’s web based system was very dull and only consisted of drop down menus.
2.3-Compared to other DSS on the module.
The majority of other DSS’s that were at the trade fair were more professional and offered more options to the customer. For example one group allowed the customer to choose the weight of their good and incorporated this into the overall quote. Another group had a facility were they could change their mark-up profit. This would be an excellent idea for a company as they could increase or decrease their prices during peak and off-peak season easily for example increase prices during the Christmas period.
3-Recommendations to enhancements of my system
Roadway Package System a competitor within Nightjar’s market has over 50 decision support applications (Turban & Aranson 1998). If nightjar were to compete they would need to implement DSS’s all over their business and make changes to their existing one.
3.1-Improvements
- Our system has one price for every customer and does not allow discounts for repeat bookings. Every customer should have a unique code and based on statistical tools, a discount could be given to certain customers when an amount of bookings have been reached.
- Our database should have advanced OLAP and data mining software, which would allow for trends and relationships to be identified. This data along side forecasting models would allow for predictions to be made on expansion opportunities, etc.
- Our database did not have the facility to save customer quotes onto a separate report for future use. This would, if implemented save time if the same quote needed to be printed or produced again.
- Our DSS should have a database for customer information. When a customer calls for a quote if we could take details relating to each customer it would be possible using data mining techniques to find trends. For example People aged 50 place larger orders and to a certain country. This would help build a customer database and segment the market for marketing purposes.
- We could have provided our users with a touch screen user interface or a Natural language processing package. Each of these would have enhanced the usability of our user interface.
- Due to the various patterns in weather I would have liked our system to have incorporated a random weather system which when producing quotes took the weather into account accordingly.
3.1-Mobile DSS
If we were to have a continuous mobile tracking system that allowed us to monitor the progress of goods by either bar coding at pre-determined intervals or using a Global Positioning System, we could operate more efficiently by anticipating how many packages are to be picked up on a specific route and whether this is possible. Also customers could monitor the progress of their package.
3.2-Web technologies
Our DSS is based within Access and can only be accessed through a computer on which the software has been installed. However if we were to implement our DSS on a web-based server then it would allow for mobile access through a browser. This would allow for continuous monitoring and inputting into the system at all times. Also customers could access the database and request quotes for themselves.
3.4-Intelligent agents
Alone side a web based DSS it would be user friendly if we provided an intelligent agent to run in the background. Then if a new user were unsure on any procedures they could be provided with help. Also the Agent could be programmed to detect patterns in user’s behaviour, which could be later analysed and corrected.
3.5-E-commerce
At the moment our system is only capable of producing quotes. If a customer where to access our DSS via a new web based server there is no facility allowing them to order our services. A good investment would be to, once the quote has been given save it and allow the customer the opportunity to place an order via Electronic data interchange facilities (ED).
3.6-Mobile commerce
With the increasing use of mobile phones it would be beneficial for us to provide access to our web functionalities via wireless application protocol (WAP). This would allow customers to constantly check the progress of their goods via a mobile phone.
4-Review of the performance of the group
4.1-Psychology of Decision Making
My approach to this project has been very prescriptive and I have taken a normative perspective on decision-making. At times the normative model I adopted was Linbolm’s (1954) cited in Teal et al. (2003) Incremental approach as this minimises risk.
I consider myself a logical and objective thinker and like to know all the options available before committing to a decision. An example being how I determined where the nodes and arches would go. I used a system where each region had a key node and feed all the other routes from these.
4.2-My perspectives
According to Berne’s (1968) cited in Teal et al. (2003) transactional analysis I was mostly of Adult ego state but sometimes had to convert to Parent when Adrian and James where acting under the Child like ego state.
4.3-Group formation
Three of our members were already acquainted so the forming stage of Tuchman’s (1965) cited in Naylor J. (1999) list was already complete for them. However as a group it took longer to form as the members who already know each other became reluctant to mix. Once the group had formed the storming and norming stages passed easily, and it was not long before roles were defined and we started to perform.
4.4-Group effectiveness
Our group worked efficiently, and everyone fully participated in-group discussions. Our group size was 5 and according to Vecchio (1995) cited in Naylor J. (1999) this is the ideal size for group participation.
We had a clear set of procedures that we followed, which included weekly meetings that were normally chaired by myself. During the meetings we would refer to Lotus notes to determine what we were meant to be discussing each week, allowing for a clear agenda to be set.
My leadership style according to Blake and Mouton’s (1985) cited in Naylor J. (1999) leadership grid was a middle of the road manager. This meant I was committed to our group’s performance while maintaining high morale. I tried were possible to empower or use reward power to keep morale high, as it was my idea to delegate roles among the group to give everyone a sense of important.
4.4-Conflict/Politics
Our group did not experience any negative inter-group conflict. However we did occasionally experience constructive conflict on functional ideas. For example how was the user-friendliest way to set up the user interface? The reason conflict was not a problem was we all had the same goals.
4.5-Climate/Culture
Within our group we all had similar cultural experiences in work and because of this we did not experience any cultural problems. According to Handy (1986) cited in Naylor J. (1999) our type of cultural was role orientated. We had specific people who could only do certain tasks, for example I would only deal with excel.
4.6-Structure
Our group structure was a flat pyramid however everyone was still connected and could communicate with one another (fig, 2). We realised from the start that James and I were more knowledgeable about the subject and it was agreed that we would take a leadership role. The decision-making was generally centralised with James or I passing down tasks. We both were democratic and tried were possible to empower the other members of the group.
Fig, 2.
4.7- Technologies used within our group
Technologies used: -
- World-Wide-Web – Our main use of this was to gather information.
- Lotus notes – This was used for us to communicate. There was a resistance from James to adopt; he could not see how the change in his working pattern would benefit him. Once we explained it was used as a back up only he used it more often.
- MSN (instant chat) – Used by Alan and myself to discuss work instantly when distance was a problem.
- Mobile phones – A problem with ICT communication is it allows for people to hold back information. We had experiences were people would miss deadlines for sending emails and we used mobile phones to verbally communicate instantly.
5-Appendices
5.1-Appendix 1 All components of a DSS.
Data: External
Internal
(Turban 1995)
5.2-Appendix 2 Structure of the Database subsystem.
(Turban 1995)
5.3-Appendix 3 Structure of the Model Base subsystem.
D
(Turban 1995)
5.4-Appendix 4 Structure of the User Interface Base subsystem
(Turban 1995)
5.5-Appendix 5 A group decision support system
Public Screen
(Turban 1995)
6-Bibliography
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Gorry G. A and Scott M. S. (1971) Slogan Management Review. Vol.13. No 1. Cited in Olson D. L. & Courtney J. F. (1992) Decision Support Models and Expert Systems. New York. Macmillan Publishing Company.
Handy C. B. (1986) Understanding Organisation (4th Ed). Harmondsworth: Penguin, 188. Cited in Naylor J. (1999) Management. Essex: Pearson Education Ltd.
Lazysoft Ltd (2003) www.lazysoft.com. Available from http://lazysoft.com/docs/other_docs/amd_whitepaper.pdf. Accessed on the 18th June 2003.
Linbolm C. (1954) The science of muddling through. Public Administration Review, 29: 79-88. Cited in Teal M,. Dispenza V., Flynn J., Currie D., (2003) Management Decision-Making Towards an Integrated Approach. Essex: Pearson Education Ltd.
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Power, D.J. (2003) A Brief History of Decision Support Systems. Version 2.8. Cited from Power A.P (2003) www.dssresources.com, Available from http://dssresources.com/history/dsshistory.html, Accessed on the 17th June, 2003.
TNT Holdings B.V. (2003) http://www.tnt.com/index_flash.html. Available from http://www.tnt.com/qlaunch/fr_WAP.html. Access on the 14th June 2003.
Tuchman R.W. (1965) Development sequence in small groups. New York: Willey. Cited in Naylor J. (1999) Management. Essex: Pearson Education Ltd.
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Vecchio R. P. (1995) Organisational Behaviour (3rd Ed). For Worth, Tex: The dryen Press 22-3. cited in Naylor J. (1999) Management. Essex: Pearson Education Ltd.
Watson H. J., Houdeshell G., Rainer R. K., (1997) Building Executive Information Systems and other Decision Support Applications. New York. John Wiley & Sons.
Whitten, J. L., & Bentley. L. B. (1997). Systems Analysis and Design Methods (4th ed). Burr Ridge, IL. Irwin. Cited in Turban E. & Aranson J. (1998). Decision Support Systems and Intelligent Systems (5th ed.) New Jersey. Prentice Hall.
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